Deciphering the role of adipose tissue in common metabolic disease via adipose tissue proteomics

通过脂肪组织蛋白质组学解读脂肪组织在常见代谢疾病中的作用

• 批准号: MR/Y013891/1
• 负责人: Kerrin Shannon Small
• 金额: $ 181.18万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FY013891%2F1
• 关键词: Deciphering; role; adipose; tissue; common

Type 2 diabetes and metabolic disease are a major cause of disease worldwide and an increasing global problem affecting economic prosperity and quality of life. While increases in obesity and lifespan in modern societies are contributors to the increased prevalence of these diseases, it is clear that genetic factors are also important in determining who develops disease and who remains healthy. Adipose tissue (fat) is an endocrine organ which plays a central role in the regulation of metabolism, inflammation, synthesis and secretion of hormones. A significant portion of inter-individual differences in susceptibility to metabolic disorders is driven by molecular processes within adipose tissue. However, which genes are implicated in development of adipose tissue dysfunction and how they link to risk of metabolic disease is not well understood. Uniquely among the main tissues implicated in metabolic disease (pancreas, muscle, fat, liver), adipose tissue is relatively accessible from research volunteers. Adipose tissue thus provides an unparalleled opportunity to investigate molecular processes in a metabolic disease-relevant tissue in longitudinal population cohorts.To address this opportunity, we have designed a project that will investigate the important relationships between metabolic health, adipose molecular function and genetic risk of disease, by unbiased profiling, in depth, of adipose tissue protein and protein post translational modification (PTM) levels, in a set of 1,000 deeply phenotyped twins, [with up to 30 years of detailed metabolic and clinical measurements]. There are many steps between genetic variants and disease, but at a cellular level it is the proteins produced from genes and the functions the proteins perform that drive disease susceptibility. Therefore, the focus of this study on deep, quantitative protein analysis in the relevant tissue will provide a major advance in the field that can reveal unique new insights. Proteins are also the target of most clinical drugs, providing a direct link to clinical intervention, while the modulation of protein function by reversible phosphorylation and other PTMs is a key feature of signalling pathways that control metabolism and that are targeted by many therapeutics. The comprehensive, systematic and unbiased global analysis of proteins and their PTMs at scale (defining 'the proteome') is only now possible thanks to recent technological advances in mass spectrometry (MS)-based methods, which can now support identification and quantification of deep proteomes (i.e. > 6,000- proteins), with isoform resolution and identification of post-translational modifications, across many hundreds of individual samples. To our knowledge, this study will produce one of the largest solid tissue proteome datasets available from a human population cohort. We will use these novel data to characterize the breadth and genetic architecture of the adipose proteome and identify its role in development of metabolic disease and associated traits. Using the classic twin model, we will estimate the heritability of each quantified protein to assess the relative contribution of genetics and environment in this unique population dataset. Combining the adipose proteome with existing genomic data will identify genetic variants regulating protein levels. We will use concurrently measured clinical phenotypes collected on the profiled twins to identify proteins linked to changes in clinically relevant traits. We will use statistical methods to integrate our results with large Biobanks to identify molecular mechanisms underlying genetic risk of metabolic traits, and to identify new genetic regions driving risk of metabolic disease. This work will provide novel insights into how metabolic problems develop and discover key genes involved, identifying important targets for future therapy and diagnostics.

2型糖尿病和代谢疾病是全球疾病的主要原因，也是影响经济繁荣和生活质量的全球问题。尽管现代社会中肥胖和寿命的增加是使这些疾病患病率增加的原因，但很明显，遗传因素在确定谁发展疾病并保持健康方面也很重要。脂肪组织（FAT）是一种内分泌器官，在代谢，炎症，合成和分泌激素的调节中起着核心作用。脂肪组织中的分子过程驱动了对代谢疾病的易感性敏感性的显着部分差异。但是，哪些基因与脂肪组织功能障碍的发展有关，以及它们如何与代谢疾病的风险联系在一起。脂肪组织在与代谢性疾病（胰腺，肌肉，脂肪，肝脏）有关的主要组织中的独特性是相对可从研究志愿者那里获得的。 Adipose tissue thus provides an unparalleled opportunity to investigate molecular processes in a metabolic disease-relevant tissue in longitudinal population cohorts.To address this opportunity, we have designed a project that will investigate the important relationships between metabolic health, adipose molecular function and genetic risk of disease, by unbiased profiling, in depth, of adipose tissue protein and protein post translational modification (PTM) levels, in a set of 1,000深层表型双胞胎，[最多30年的详细代谢和临床测量]。遗传变异和疾病之间有许多步骤，但是在细胞水平上，它是由基因产生的蛋白质，而蛋白质的作用可促进疾病敏感性。因此，这项研究的重点是相关组织中深层的定量蛋白质分析，将在该领域提供重大进步，可以揭示独特的新见解。蛋白质也是大多数临床药物的靶标，它提供了与临床干预的直接联系，而通过可逆磷酸化和其他PTMS对蛋白质功能的调节是控制代谢的信号通路的关键特征，并且许多治疗疗法都针对许多疗法。 The comprehensive, systematic and unbiased global analysis of proteins and their PTMs at scale (defining 'the proteome') is only now possible thanks to recent technological advances in mass spectrometry (MS)-based methods, which can now support identification and quantification of deep proteomes (i.e. > 6,000- proteins), with isoform resolution and identification of post-translational modifications, across many hundreds of individual samples.据我们所知，这项研究将产生最大的固体组织蛋白质组数据集之一，可从人口组中获得。我们将使用这些新颖的数据来表征脂肪蛋白质组的广度和遗传结构，并确定其在代谢疾病和相关性状的发展中的作用。使用经典的双胞胎模型，我们将估计每种定量蛋白质的遗传力，以评估这个独特的人群数据集中遗传学和环境的相对贡献。将脂肪蛋白质组与现有基因组数据相结合将鉴定调节蛋白质水平的遗传变异。我们将使用在剖面双胞胎上收集的同时测量的临床表型来识别与临床相关性状变化相关的蛋白质。我们将使用统计方法将结果与大生物库集成，以鉴定代谢性状的遗传风险的分子机制，并确定促进代谢疾病风险的新遗传区域。这项工作将提供有关代谢问题如何发展和发现关键基因的新见解，从而确定了未来治疗和诊断的重要目标。